Previous studies on the human likeness of service robots have focused mainly on their human-like appearance and used psychological constructs to measure the outcomes of human likeness. Unlike previous studies, this study focused on the human-like behavior of the service robot and used a sociological construct, social distance, to measure the outcome of human likeness. We constructed a conceptual model, with perceived competence and warmth as mediators, based on social-identity theory. The hypotheses were tested through online experiments with 219 participants from China and 180 participants from the US. Similar results emerged for Chinese and American participants in that the high (vs. low) human-like behavior of the service robot caused the participants to have stronger perceptions of competence and warmth, both of which contributed to a smaller social distance between humans and service robots. Perceptions of competence and warmth completely mediated the positive effect of the human-like behavior of the service robot on social distance. Furthermore, Chinese participants showed higher anthropomorphism (perceived human-like behavior) and a stronger perception of warmth and smaller social distance. The perception of competence did not differ across cultures. This study provides suggestions for the human-likeness design of service robots to promote natural interaction between humans and service robots and increase human acceptance of service robots.
High‐quality broadband data are required to promote the development of seismology research. Instrument response errors that affect data quality are often difficult to detect from visual waveform inspection alone. Here, we propose a method that uses ambient noise data in the period range of 5−25 s to monitor instrument performance and check data quality in situ. Amplitude information of coda waves and travel time of surface waves extracted from cross‐correlations of ambient noise are used to assess temporal variations in the sensitivity and poles–zeros of instrument responses. The method is based on an analysis of amplitude and phase index parameters calculated from pairwise cross‐correlations of three stations, which provides multiple references for reliable error estimates. Index parameters calculated daily during a two‐year observation period are evaluated to identify stations with instrument response errors in real time. During data processing, initial instrument responses are used in place of available instrument responses to simulate instrument response errors, which are then used to verify our results. The coda waves of noise cross‐correlations help mitigate the effects of a non‐isotropic field and make the amplitude measurements quite stable. Additionally, effects of instrument response errors that experience pole–zero variations on monitoring temporal variations in crustal properties appear statistically significant of velocity perturbation and larger than the standard deviation. Monitoring seismic instrument performance helps eliminate data pollution before analysis begins.
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